Analysis of risk factors for pulmonary infection in adults with newly treated acute leukemia

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Methods This retrospective study analyzed the clinical data and laboratory findings of patients newly diagnosed with acute leukemia, admitted to the Department of Hematology at Yichang Central People's Hospital between September 2018 and November 2023. Risk factors for pulmonary infection and the distribution of pulmonary pathogens were evaluated. Results A total of 251 patients with acute leukemia were enrolled in the study, comprising 62 cases of acute lymphoblastic leukemia and 189 cases of acute myeloid leukemia. Pulmonary infections developed in 200 patients, corresponding to an infection rate of 79.68%. Multivariate analysis revealed that prolonged hospitalization, smoking history, extended duration of agranulocytosis, and failure to achieve complete remission following chemotherapy were independent risk factors for pulmonary infection (P < 0.05). In contrast, prophylactic antibiotic use prior to chemotherapy was identified as a protective factor. Among the 200 patients with pulmonary infections, 51 pathogenic isolates were identified. The distribution of pathogens was as follows: bacteria (39.22%), viruses (34.55%), fungi (20.0%), and Mycoplasma (1.82%). Conclusions In patients receiving initial chemotherapy for acute leukemia, prolonged hospitalization, extended duration of agranulocytosis, failure to achieve complete remission, and a history of smoking were associated with an elevated risk of pulmonary infection. Gram-negative bacteria represented the predominant pathogens. Early identification, timely intervention, and appropriate selection of antimicrobial agents are critical to reducing mortality in this population. Acute leukemia pulmonary infection risk factors smoking history novel coronavirus infection Introduction Acute leukemia (AL) comprises a heterogeneous group of hematologic malignancies arising from disrupted differentiation of hematopoietic stem cells, and is characterized by blocked differentiation, impaired apoptosis, and malignant proliferation. Common clinical features include anemia, hemorrhage, fever, and hepatosplenomegaly or lymphadenopathy [ 1 , 2 ]. Pathologically, AL is categorized into acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) according to cell lineage. Chemotherapy for AL often results in complications such as myelosuppression, which markedly elevates the risk of infection. Infections represent a leading cause of treatment-related morbidity and mortality in AL patients and frequently necessitate interruption of therapy [ 3 – 5 ]. The specific risk factors for pulmonary infection following chemotherapy in AL patients remain a subject of debate, and epidemiological patterns exhibit notable regional differences [ 6 ]. Furthermore, few studies have specifically addressed pulmonary infections in adult AL patients undergoing initial induction chemotherapy. This study aims to analyze the risk factors for pulmonary infection in treatment-naïve adult AL patients in the Yichang region, with the goal of informing early preventive strategies against post-chemotherapy pulmonary infections. Methods Patient population A total of 251 adult patients with newly diagnosed acute leukemia were retrospectively included in this study. These patients were hospitalized in the Department of Hematology at Yichang Central People's Hospital between September 2018 and November 2023. All participants had complete medical records and underwent the induction phase of chemotherapy at our institution. The inclusion criteria were as follows: ① age ≥ 18 years; ② diagnosis consistent with established diagnostic criteria for acute leukemia [ 7 , 8 ]; and ③ completion of induction chemotherapy at our hospital. Exclusion criteria comprised the following: ① Age under 18 years; ② Diagnosis of acute promyelocytic leukemia; ③ Pre-existing bloodstream infection, active pulmonary tuberculosis, non-infectious interstitial lung disease, pulmonary edema, pulmonary eosinophilia, or pulmonary vasculitis prior to treatment; ④ History of psychiatric or cognitive disorders; ⑤ Previous diagnosis of other malignant tumors; ⑥ Severe comorbidities involving major organs (e.g., heart, lungs, kidneys); ⑦ Coexisting hematologic diseases. Systematically collected data encompassed demographic information (age, sex), clinical features (comorbidities, length of hospital stay, acute leukemia subtype, smoking history, pre-existing comorbidities, corticosteroid use, and antibiotic exposure prior to chemotherapy), laboratory values (at diagnosis: peripheral white blood cell count, absolute neutrophil count, absolute lymphocyte count, hemoglobin, and platelet count; during chemotherapy: neutrophil-to-lymphocyte ratio, hemoglobin/leukocyte/neutrophil/platelet counts, albumin, C-reactive protein, procalcitonin, and duration of agranulocytosis [absolute neutrophil count < 0.5 × 10⁹/L]), infection details (bacterial identification), use of invasive devices (central venous catheter [CVC] or peripherally inserted central catheter [PICC]), and bronchoalveolar lavage fluid analysis. Definitions The diagnostic criteria for pulmonary infection were established based on the definitions outlined in the 10th edition of *Internal Medicine* (People's Medical Publishing House). Patients were included if they fulfilled the following criteria: ① new or worsening cough and sputum production, with or without chest pain; ② body temperature ≥ 38.0°C; ③ physical signs of pulmonary consolidation and/or moist rales; ④ white blood cell count > 10×10⁹/L or < 4×10⁹/L, with or without a left shift; and ⑤ chest CT findings consistent with pulmonary lesions, such as patchy, consolidative, or cavitary opacities. A clinical diagnosis of pulmonary infection—encompassing infections present before chemotherapy—was made if at least one of criteria (1) to (4) was met along with criterion (5), following the exclusion of other conditions such as pulmonary tuberculosis, lung malignancy, non-infectious interstitial lung disease, pulmonary edema, and atelectasis. The criteria for assessing induction remission efficacy in leukemia were defined as follows. Treatment response was classified into three categories: complete remission (CR), partial remission (PR), and non-remission (NR), with the specific criteria outlined below: Complete Remission (CR): Bone marrow blast count < 5%, accompanied by the disappearance of blasts in peripheral blood and resolution of extramedullary lesions. Hematologic recovery is not a requirement for this category. Partial Remission (PR):Bone marrow blast percentage must decrease by at least 50%, resulting in a blast proportion between 5% and 25%. In addition, an absolute neutrophil count exceeding 1×10⁹/L and a platelet count of at least 100×10⁹/L must be achieved. Non-Remission (NR): Cases that do not satisfy the criteria for either CR or PR are classified as NR. Statistical methods Categorical variables are reported as frequencies (percentages), with between-group comparisons conducted using the χ² test or Fisher’s exact test, as appropriate. Continuous data that followed a normal distribution are expressed as mean ± standard deviation (SD) and compared via Student’s t-test; non-normally distributed data are summarized as median (interquartile range, IQR) and analyzed using the Mann–Whitney U test. To identify independent influencing factors, univariable analysis was initially conducted for all candidate variables. Variables with a P-value < 0.05 were entered into a multivariable logistic regression model. To reduce the risk of overfitting, forward stepwise regression was employed for variable selection, using a P-value < 0.05 for both entry and removal. All statistical tests were two-sided, and a P-value < 0.05 was considered statistically significant. Results Patient characteristics This study enrolled a total of 251 patients with newly diagnosed acute leukemia (AL) during their hospitalization. As shown in Table 1 , univariate analysis was performed on the adult cohort. Of the 251 patients receiving initial induction chemotherapy, pulmonary infections occurred in 200 cases, corresponding to an infection rate of 79.68% (200/251). The study population comprised 189 patients (75.3%) with acute myeloid leukemia (AML) and 62 (24.7%) with acute lymphoblastic leukemia (ALL). There were 135 males (53.8%) and 116 females (46.2%), with a median age of 55 years. A smoking history was reported in 89 patients (35.46%), among whom 80 (89.89%) developed pulmonary infection. Corticosteroid therapy was administered to 94 patients (37.45%).Of the 256 patients evaluated, 98 (39.04%) had active disease, while 153 (60.96%) had achieved remission. Univariate analysis indicated that several factors were significantly associated with pulmonary infection (P < 0.05), including total hospital stay, smoking history, neutrophil count before chemotherapy, prophylactic antibiotic use prior to chemotherapy, duration of agranulocytosis, nadir blood cell counts (WBC, NEC, LYM, NLR, PLT), serum albumin level, failure to achieve complete remission after chemotherapy, hypoalbuminemia, and elevated levels of C-reactive protein and procalcitonin (PCT) (Table 1 ). Multivariable logistic regression analysis identified prolonged total hospital stay, smoking history, extended duration of agranulocytosis, and failure to achieve complete remission as independent risk factors for pulmonary infection (P < 0.05). In contrast, prophylactic antibiotic use before chemotherapy was found to be a protective factor (P < 0.05), further corroborating the robustness of our main findings (Table 2 ). Bacterial distribution The primary etiological agents of pulmonary infection in patients with acute leukemia were fungi (45.98%), followed by bacteria (20.69%), mixed infections (17.24%), viruses (14.94%), and mycoplasma (1.15%). Among the bacterial isolates, Gram-positive organisms were identified in 7 cases (8.05%), comprising *Staphylococcus aureus (3, 3.45%), *Streptococcus pneumoniae (2, 2.30%), Staphylococcus epidermidis (2, 2.30%), and Staphylococcus capitis (1, 1.15%). Gram-negative bacteria accounted for 11 cases (12.64%), with the majority being Klebsiella pneumoniae (5, 5.75%), Pseudomonas aeruginosa (4, 4.60%), Escherichia coli (2, 2.30%), and Haemophilus influenzae (1, 1.15%). A total of 40 fungal pathogens were isolated, including Aspergillus (7, 8.05%), Candida albicans (1, 1.15%), Candida krusei (1, 1.15%), Candida tropicalis (1, 1.15%), *Saccharomyces cerevisiae (1, 1.15%), clinically diagnosed fungi (18, 20.69%), and suspected fungal infections (11, 12.64%). Viral pathogens were detected in 13 cases, consisting of influenza virus (7, 8.05%), SARS-CoV-2 (4, 4.60%), and Epstein-Barr virus (2, 2.30%). One case (1.15%) of Mycoplasma pneumoniae was also documented (Table 3 ). Table 1 univariate of adults with newly treated acute leukemia variables Pulmonary infection cases(n = 200) Controls (n = 51) p-value Age(year) 55(45,64) 59(48,65) 0.405 Male, n(%) 112(56) 23(45.1) 0.163 AL classification AML 155 34 0.109 ALL 45 17 Smoking(%) 74(37) 8(15.7) 0.004 Hypertension(%) 40(20) 10(19.6) 0.950 diabetestan(%) 24(12) 5(9.8) 0.661 Placement of picc(%) 116(58) 26(50.9) 0.367 Corticosteroid treatment(%) 72(36) 22(43.1) 0.347 Administration of antibiotics prior to chemotherapy.(%) 109(54.5) 11(21.5) <0.001 Length of hospitalization (days) 29(25,35) 26(22,30) 0.01 Previous use chemotherapy of blood count WBC 17.47(5.08,55.48) 21.48(5.29,76.82) 0.258 Hb 72(59.0,86.0) 70(50.0,88.0) 0.614 Platelet 32(15.0,63.0) 47(24.0,87.0) 0.051 Neutrophile 1.39(0.39,4.79) 3.17(1.53,6.50) 0.003 Blood count during chemotherapy WBC 0.38(0.21,0.68) 1.10(0.60,1.64) <0.001 Hb 54(49.0,61.0) 57(46.0,61.0) 0.315 Platelet 10(4.0,23.0) 15(7.0,31.0) 0.022 Neutrophile 0.02(0.01,0.11) 0.25(0.06,0.61) <0.001 Lymphocyte(×10^12/L) 0.28(0.17,0.41) 0.59(0.29,0.84) <0.001 NLR 0.12(0.03,0.35) 0.35(0.10,0.90) <0.001 Albumin 29.57(26.74,33.21) 31.95(29.23,36.84) 0.001 C reactive protein 88.31(56.87,127.79) 28.20(8.26,47.94) <0.001 Pct(ng/ml) 0.28(0.12,0.80) 0.12(0,0.26) <0.001 Median Duration of neutropenia(days) 17.47(5.08,55.48) 4(0,11) <0.001 Disease status In remission 110 43 <0.001 Not in remission 90 8 * Abbreviations: PCT: procalcitonin ; Table 2 Multivariate analysis of the risk factors for lung infection during induction chemotherapy among patients with newly diagnosed acute leukemia. variable or 95%CI p-value Length of Stay 1.073 1.004—1.147 0.039 Smoking 2.931 0.122—0.952 0.040 Previous use chemotherapy of blood count Neutrophile 1.015 0.984—1.048 0.343 Previous use of Antibiotic treatment 0.145 2.629—18.121 <0.001 Median Duration of neutropenia 1.213 1.123—1.309 <0.001 Blood count during chemotherapy WBC 1.254 0.9—1.747 0.182 Neutrophile 1.038 0.212—5.078 0.963 Lymphocyte(×10^12/L) 0.518 0.216—1.239 0.139 NLR 1.133 0.523—2.457 0.751 Platelet 0.991 0.98—1.001 0.088 Albumin 1.054 0.986—1.126 0.120 Not in remission 4.406 1.556—12.475 0.005 *A P-value of < 0.05 was considered to be statistically significant. OR = odds ratio; CI = confidence interval. Table 3 Distribution of pathogens Bacterial Distribution n percentage (%) Gram-negative bacteria Klebsiella pneumoniae 5 9.80% Pseudomonas aeruginosa 4 7.84% Escherichia coli 2 3.64% Haemophilus influenzae 1 1.82% Gram-positive bacteria Staphylococcus aureus 3 5.45% Streptococcus pneumoniae 2 3.64% Staphylococcus epidermidis 2 3.64% Staphylococcus capitis 1 1.82% Fungal pathogens Aspergillus 7 8.05% Candida albicans 1 1.15% Candida Cruz 1 1.15% Candida tropical 1 1.15% Saccharomyces cerevisiae 1 1.15% Clinical diagnosis of fungi 18 20.69% Suspected fungal 11 12.64% Viral pathogens Influenza virus 7 8.05% Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 4 4.60% Epstein-Barr virus (EBV) 2 2.30% Mycoplasma pneumoniae 1 1.15% Mixed infection Novel coronavirus + Aspergillus 10 11.49% Aspergillus + human rhinovirus 2 2.30% Novel coronavirus + clinical diagnosis of fungi 1 1.15% Aspergillus + Staphylococcus epidermidis 1 1.15% Aspergillus + Pseudomonas aeruginosa 1 1.15% Discussion Infection continues to be a major contributor to morbidity and mortality in patients with acute leukemia [ 9 ]. Induction chemotherapy often extends over a prolonged period, increasing the risk of complications such as myelosuppression. Additionally, acute leukemia directly impairs humoral and cellular immune function, weakening host defenses. The sustained administration of chemotherapeutic agents further compromises patient immunity by damaging mucosal barriers in the gastrointestinal and respiratory tracts. Among infectious complications, pulmonary infections are particularly common. A retrospective analysis revealed that pulmonary infections following induction chemotherapy for acute leukemia occur frequently, with a cumulative 28-day incidence of 21.6% in AML patients and 7.1% in those with ALL; mortality associated with these pulmonary infections reached 17% [ 10 ]. Another retrospective study from the Japanese Leukemia Research Institute, which included 908 acute leukemia patients during induction therapy, reported bacteremia and respiratory tract infection rates of 8.3% and 10.3%, respectively [ 11 ]. Existing evidence indicates considerable regional variation in infection incidence among acute leukemia patients. These geographical differences in overall infection rates are likely multifactorial, possibly influenced by individual behaviors, social environment, baseline health status, hospital resources, pathogen evolution, and population mobility [ 12 ]. This study included 251 treatment-naïve patients with acute leukemia admitted to the Department of Hematology. Of these, 200 patients (79.68%) developed pulmonary infections, whereas 51 (20.32%) did not. Univariate analysis of patient demographic and laboratory indicators, supplemented by logistic regression, identified several independent risk factors for pulmonary infection: longer total hospital stay, smoking history, prolonged agranulocytosis duration, and failure to achieve disease remission following chemotherapy. In contrast, antibiotic administration prior to chemotherapy served as a protective factor against pulmonary infection. The hospital environment is a known reservoir for bacterial contamination. Extended hospitalization increases opportunities for pathogen colonization, transmission, and cross-infection. When pathogens invade the lower respiratory tract in acute leukemia patients, they can inflict structural and functional damage to pulmonary and tracheal tissues. Additionally, various inpatient procedures—such as intubation, puncture, and surgery—may heighten infection risks. In this study, the presence of a PICC line showed no statistically significant association with infection incidence, which may be closely linked to the quality of nursing care. Timely intervention and meticulous nursing have the potential to shorten hospital stays and consequently reduce infection risk. Effective disease management, appropriate preventive measures—including judicious antibiotic use, maintenance of normothermia and adequate hydration, and the use of laminar airflow rooms—are essential in this context. International literature indicates that early discharge is a feasible, safe, and cost-effective approach for acute leukemia patients who are in good clinical condition and free of complications [ 13 , 14 ]. Furthermore, both domestic and international studies consistently document a strong association between prolonged neutropenia and increased infection risk [ 15 – 17 ]. The malignant proliferation of leukemic cells in the bone marrow leads to impaired immune function. Chemotherapy may exacerbate neutropenia, thereby elevating patients’ susceptibility to severe infections and increasing mortality risk. Persistent neutropenia requires vigilant monitoring, as it heightens the likelihood of bacterial, fungal, and other opportunistic infections. Owing to compromised immune defenses, such patients are at risk of rapid clinical decline and fatal infectious complications. Upon signs of infection, hospitalization and prompt empiric broad-spectrum antibiotic therapy are typically indicated, even prior to identifying the specific pathogen or infection site. In contemporary clinical practice, granulocyte colony-stimulating factor (G-CSF) is widely employed to enhance neutrophil production, thereby aiding in the prevention of febrile episodes, oral mucositis, and bacterial infections in neutropenic patients. Consistent with global findings, pre-chemotherapy antibiotic prophylaxis was identified as a significant protective factor against pulmonary infection in our cohort (P < 0.001). Relevant literature indicates that systemic antibacterial prophylaxis has emerged as an effective strategy for minimizing infection-related complications [ 18 ]. Our results demonstrate that prophylactic antibiotic therapy serves as a protective factor, significantly correlated with decreased overall mortality and a lower incidence of pulmonary infection among patients with acute leukemia. Antibiotics are essential in the management of pulmonary infections, functioning by eliminating or suppressing pathogenic microorganisms. Treatment is tailored according to infection severity, pathogen profile, and patient-specific conditions, with clinicians determining the appropriate antibiotic type and dosing regimen accordingly. The selection of suitable antibiotics must account for pathogen susceptibility, antimicrobial efficacy, and individual patient factors. However, inappropriate use of antibiotics may contribute to adverse outcomes, including the emergence of drug-resistant strains [ 19 , 20 ]. Thus, rational antibiotic use, guided by local antimicrobial susceptibility data and clinical presentation, is critical to mitigating antibiotic resistance and improving patient survival. Patients who fail to achieve complete remission often exhibit suppressed normal hematopoiesis and are susceptible to prolonged agranulocytosis after induction chemotherapy, which contributes to increased rates of infection and mortality [ 10 , 21 ]. According to Luo et al., the pulmonary infection rate reached 81.0% among acute leukemia patients without remission [ 22 ]. Compared with those in complete remission, patients with persistent disease present with higher tumor burden and unimproved cellular immune function, leading to a markedly elevated risk of fatal infections. In the present study, pulmonary infections occurred in 74 of 82 patients (90.24%) with a history of smoking. A significantly higher incidence of infection was observed in smokers relative to non-smokers (P = 0.004).Cigarette smoke consists of nicotine, nitric oxide, free radicals, acetaldehyde, and other compounds. Inhaled particles of different sizes deposit along the respiratory tract, with larger particles retained in the upper airways and finer particles penetrating to the alveolar regions. Exposure to cigarette smoke leads to morphological alterations in the bronchial mucosal epithelium, such as ciliary loss, mucus gland hypertrophy, and goblet cell hyperplasia, which can facilitate microbial proliferation and spread within the bronchi. Smoking is also known to suppress essential functions of both innate and adaptive immunity, including Toll-like receptor signaling, nuclear factor kappa B activation, CD4 + T-cell proliferation, dendritic cell maturation, opsonization, and phagocytic activity [ 23 ]. Consequently, smoking not only modifies immune responses to infections but also disrupts intracellular and intercellular signaling pathways in epithelial and immune cells, thereby inhibiting the activation of key immune components. It is well established that smoking heightens susceptibility to infections [ 23 ]. Furthermore, our analysis revealed that among patients with a history of smoking, fungal infections were detected in 32 cases, accounting for 74.41% (32/43) of all fungal infections in the cohort. The likelihood of fungal infection was significantly higher in smokers than in non-smokers (P = 0.032), a result consistent with findings from international studies [ 24 , 25 ]. In cases of acute leukemia complicated by pulmonary infections, bacterial pathogens represent the most frequently identified etiological agents, with Gram-negative bacteria being predominant [ 22 , 26 , 27 ]. In the current study, a total of 51 pathogenic isolates were identified. The pathogen distribution was as follows: bacteria were the most common, followed by viruses, fungi, and Mycoplasma. Among bacterial isolates, Gram-negative bacilli comprised the majority, representing 23.53% (12/51) of all isolates. The most frequently identified pathogen was Klebsiella pneumoniae, followed by Pseudomonas aeruginosa, Escherichia coli, and Haemophilus influenzae. Gram-positive cocci constituted 14.55% (8/55) of the isolates, including Staphylococcus aureus, Streptococcus pneumoniae, Staphylococcus epidermidis, and Staphylococcus capitis. The relatively low pathogen detection rate observed in this study may be explained by several factors: 1) suboptimal specimen quality (e.g., sputum, throat swabs) obtained from acute leukemia patients, which may have contained insufficient pathogen loads—particularly if antibiotics were administered before sample collection, potentially diminishing pathogen counts; 2) delays in transporting specimens to the laboratory, which could result in pathogen degradation and false-negative outcomes; and 3) limitations associated with the detection methodologies available at our institution. Certain pathogens may be present in low abundances or require specific growth conditions not routinely used. To enhance the diagnostic yield for pulmonary infections, several strategies should be implemented. First, it is essential to raise clinical awareness among healthcare providers about the significance of microbiological diagnosis. Suspected infections should be carefully monitored, and appropriate specimens for pathogen identification must be collected promptly and repeatedly before empirical antibiotic therapy is initiated. In parallel, laboratory detection equipment and methodologies should be regularly evaluated and upgraded to improve both the detection rate and accuracy of pathogen identification. The occurrence of viral infections in patients with acute leukemia represents a serious concern that requires close attention, as these infections can precipitate rapid clinical decline and life-threatening complications. During chemotherapy, such patients are at heightened risk of viral infections due to treatment-related agranulocytosis, myelosuppression, and subsequent profound immunosuppression. Individual genetic predispositions can modulate susceptibility to particular viral pathogens. For example, specific haplotypes of the *DEFB1* gene have been linked to the prevalence of herpesviruses in serum [ 28 ]. According to research by Hlaing K M et al. [ 29 ], acute leukemia patients infected with SARS-CoV-2 are more susceptible to co-infections with other pathogens. Patients with severe or critical COVID-19 frequently receive high-dose corticosteroids and immunosuppressive therapies. Given that SARS-CoV-2 infection can directly induce lymphopenia, immune function becomes further compromised, increasing the risk of secondary infections. This interaction has prompted the emerging concept of the lung microbiome. In healthy individuals, the lungs maintain a relatively stable microbial community; however, SARS-CoV-2 infection disrupts this balance, facilitating the proliferation of other bacteria, viruses, and fungi, and leading to a dynamically altered microbiome. This shift may lead to two major clinical implications. First, the occurrence of co-infection with other pathogens complicates subsequent diagnosis and treatment. Second, interactions between SARS-CoV-2 and other pathogens can induce pulmonary immune dysregulation and worsen disease severity. Supporting evidence comes from a 2020 single-center study in Milan, Italy, which included 731 hospitalized COVID-19 patients (moderate, severe, and critical cases) and reported a secondary infection rate of 9.3% (80/731). Among these patients, bloodstream infections accounted for 7.9% (58/731) and lower respiratory tract infections for 3% (22/731). The median time to secondary infection was 12 days (IQR 8.5–16.5 days), which decreased to 9 days (IQR 7–17 days) in patients receiving immunomodulatory drugs. Subgroup analysis indicated that patients with baseline lymphopenia (≤ 0.7×10⁹/L) or a low baseline PaO₂/FiO₂ ratio (≤ 200 mmHg) had a higher likelihood of developing secondary infections [ 30 ]. A multicenter study conducted in Shanghai, Wuhan (Hubei), and Tongling (Anhui) evaluated 476 COVID-19 patients by comparing clinical characteristics, laboratory results, imaging findings, and treatment regimens. Patients were categorized into four groups according to symptom severity, extent of lung involvement on chest CT, degree of hypoxemia, and need for respiratory support. The analysis revealed that disease severity was associated with an increased incidence of secondary infections [ 31 ]. In another investigation from Shanghai Huashan Hospital involving 38 severe or critical COVID-19 patients—most of whom underwent endotracheal intubation or tracheostomy—57.89% (22/38) developed secondary infections. Multivariate analysis identified invasive mechanical ventilation as a significant risk factor. Affected patients experienced considerably longer hospital stays and worse clinical outcomes [ 32 ]. Together, these results suggest that COVID-19 patients, especially those with severe manifestations, are at elevated risk of secondary infections. Once co-infections occur, they may lead to marked clinical worsening and substantially increase mortality risk. Treatment-naïve acute leukemia patients exhibit heightened susceptibility to pulmonary infections during induction chemotherapy—a frequent complication closely linked to severe myelosuppression and immunosuppression. Proactive strategies to enhance immune defenses, including prophylactic antibiotic regimens, placement in laminar airflow environments, and optimized supportive care, should be implemented to reduce infection risk. Nevertheless, close monitoring remains critical, as extended antibiotic use elevates the likelihood of fungal infections, warranting serial surveillance via diagnostic assays such as G-tests and GM-tests. Therefore, a personalized therapeutic strategy is essential. Meticulous monitoring of laboratory parameters before and after treatment initiation is critical to facilitate earlier and more individualized clinical interventions. In subsequent research, we intend to gather broader clinical datasets and integrate additional analytical factors—such as genetic markers that may clarify the relationship between acute leukemia susceptibility and pulmonary infection risk, as well as associations between host genetics and the structure of the pulmonary microbiota. These initiatives are expected to better characterize the multifactorial determinants of pulmonary infection during induction therapy. Ultimately, risk-based patient stratification will enable the implementation of precisely tailored management approaches. Such a precision medicine framework holds considerable potential for reducing the emergence of antimicrobial resistance and meaningfully enhancing patients’ quality of life. Declarations Funding The authors have not received any funding during the creation of this manuscript. The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China Data availability No datasets were generated or analysed during the current study. Ethical and consent to publish Not applicable. Competing interests The authors declare no competing interests. Data availability The data presented in this study are available on request from the corresponding author. Declarations Ethical approval The study protocol was approved by the Institutional Review Boards (IRBs) of The First College of Clinical Medical Science of Three Gorges University (approval reference number: 2022-111-01). Since retrospective data were used, the IRB of The First College of Clinical Medical Science of Three Gorges University waived the requirement for informed consent. This research complied with the ethical principles for medical research declared by the World Medical Association, and the study was conducted in compliance with the Helsinki Declaration of 1964 and all of its amendments. All patient data was kept confidential and secured. The collected patient informa tion was used only for research purposes. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author Contribution Li-jing Wang and Hui-li Cai designed the study. Li-jing Wang analyzed the data and generated the tables and figures. Qi Gao, Bo Pang, Tao Wu, Jun Xiang，Liming Xiang ，Kuanxin Zhang collected and recorded the data. Li-jing Wang wrote the study. Hui-li Cai: Conceptualization, project administration, Writing– review & editing. Confficts of interest. All authors contributed to the article and approved the submitted version. References Ladikou EE, Ashworth I, Seviar D et al (2022) Acute leukaemia: no reason to panic. 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Arch Bronconeumol 50(6):250–254 Khandelwal A, Chaudhary SC, Verma AK et al (2023) Nontubercular bacterial and fungal infections in patients of chronic obstructive pulmonary disease. Ann Afr Med 22(1):77–81 Stemler J, Mellinghoff SC, Khodamoradi Y et al (2023) Primary prophylaxis of invasive fungal diseases in patients with haematological malignancies: 2022 update of the recommendations of the Infectious Diseases Working Party (AGIHO) of the German Society for Haematology and Medical Oncology (DGHO). J Antimicrob Chemother 78(8):1813–1826 You XL, Ma D (2024) [Analysis of risk factors for pulmonary infection after chemotherapy in patients with acute leukemia and establishment of a risk nomogram model]. Shi Yong Yu Fang Yi Xue 31(1):39–43 Ahmadzadeh A, Varnasseri M, Jalili MH et al (2013) Infection Pattern of Neutropenic Patients in Post-chemotherapy Phase of Acute Leukemia Treatment. Hematol Rep 5(4):e15 Moschovi M, Adamaki M, Vlahopoulos SA (2016) Progress in Treatment of Viral Infections in Children with Acute Lymphoblastic Leukemia. Oncol Rev 10(1):300 Hlaing KM, Monday LM, Nucci M et al (2023) Invasive Fungal Infections Associated with COVID-19. J Fungi (Basel). ;9(6) Ripa M, Galli L, Poli A et al (2021) Secondary infections in patients hospitalized with COVID-19: incidence and predictive factors. Clin Microbiol Infect 27(3):451–457 Feng Y, Ling Y, Bai T et al (2020) COVID-19 with Different Severities: A Multicenter Study of Clinical Features. Am J Respir Crit Care Med 201(11):1380–1388 Zhang H, Zhang Y, Wu J et al (2020) Risks and features of secondary infections in severe and critical ill COVID-19 patients. Emerg Microbes Infect 9(1):1958–1964 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8621539","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593150091,"identity":"7f661eee-489b-4186-b277-f694378cf8ac","order_by":0,"name":"lijing wang","email":"","orcid":"","institution":"The Sixth Hospital of Wuhan, Affiliated Hospital of Jianghan University","correspondingAuthor":false,"prefix":"","firstName":"lijing","middleName":"","lastName":"wang","suffix":""},{"id":593150092,"identity":"16ea0b4c-15db-4bf1-a814-4d71b285f2a7","order_by":1,"name":"Qi Gao","email":"","orcid":"","institution":"Hubei Provincial Corps Hospital of Chinese People’ s Armed Police Force","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Gao","suffix":""},{"id":593150093,"identity":"9cf2a25d-9e18-4e65-acaf-421f3729cb5d","order_by":2,"name":"Bo Pang","email":"","orcid":"","institution":"The Sixth Hospital of Wuhan, Affiliated Hospital of Jianghan University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Pang","suffix":""},{"id":593150094,"identity":"1ae80a1c-7d6e-4630-a4df-facc9f36deed","order_by":3,"name":"Jun Xiang","email":"","orcid":"","institution":"The First College of Clinical Medical Science, China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Xiang","suffix":""},{"id":593150095,"identity":"753b1d4b-fa23-4360-9559-b49ee5ec19a9","order_by":4,"name":"Tao Wu","email":"","orcid":"","institution":"The Sixth Hospital of Wuhan, Affiliated Hospital of Jianghan University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Wu","suffix":""},{"id":593150096,"identity":"3415f420-4420-4033-b3e3-6a6f38602191","order_by":5,"name":"liming Xiang","email":"","orcid":"","institution":"The Sixth Hospital of Wuhan, Affiliated Hospital of Jianghan University","correspondingAuthor":false,"prefix":"","firstName":"liming","middleName":"","lastName":"Xiang","suffix":""},{"id":593150097,"identity":"59abaa9e-3aab-48d9-9983-cb0aa5a7958d","order_by":6,"name":"Kuanxin Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Jianghan University,","correspondingAuthor":false,"prefix":"","firstName":"Kuanxin","middleName":"","lastName":"Zhang","suffix":""},{"id":593150098,"identity":"1317681b-2114-480d-975b-ee40d9e7165d","order_by":7,"name":"huili cai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3QvQrCMBDA8ZNApoOsJ+o7BAJVUPRZSkFXR0dBaDddFXyIPkKkg0vBtaOl0EUHH0DQ+gVOMW6C+UOGwP3IEQCX6yerhQCyg+J1nVoSwvqUWRPg1SGQ2paIKAkLHFNTbXfJAaHXijUr9yZCqR8prBbz0oB3EYYq1rwtTUSCHzbuJGNcISR+rJGTkYj8QdTyTi4WhJ6vSGKsQNCfCWV5WF9XhNLAq61loFYJ94xELEYlHc+9gYg2xek46bfm21lpJO9V+9y+itnO32ZPXwy7XC7XH3UFsko882MaOSoAAAAASUVORK5CYII=","orcid":"","institution":"The First College of Clinical Medical Science, China Three Gorges University","correspondingAuthor":true,"prefix":"","firstName":"huili","middleName":"","lastName":"cai","suffix":""}],"badges":[],"createdAt":"2026-01-16 18:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8621539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8621539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106068611,"identity":"b560d1cd-facc-4d0e-84f7-e6920c95fb81","added_by":"auto","created_at":"2026-04-03 06:11:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":735361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8621539/v1/52686446-19c0-4118-b72c-26d91a06d9f2.pdf"},{"id":103061764,"identity":"5555e09c-3b46-4aa4-91d1-1312aef2fb82","added_by":"auto","created_at":"2026-02-20 10:11:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26411,"visible":true,"origin":"","legend":"","description":"","filename":"file.docx","url":"https://assets-eu.researchsquare.com/files/rs-8621539/v1/fafb24f0d9d6dad1f165a220.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of risk factors for pulmonary infection in adults with newly treated acute leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute leukemia (AL) comprises a heterogeneous group of hematologic malignancies arising from disrupted differentiation of hematopoietic stem cells, and is characterized by blocked differentiation, impaired apoptosis, and malignant proliferation. Common clinical features include anemia, hemorrhage, fever, and hepatosplenomegaly or lymphadenopathy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Pathologically, AL is categorized into acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) according to cell lineage. Chemotherapy for AL often results in complications such as myelosuppression, which markedly elevates the risk of infection. Infections represent a leading cause of treatment-related morbidity and mortality in AL patients and frequently necessitate interruption of therapy [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The specific risk factors for pulmonary infection following chemotherapy in AL patients remain a subject of debate, and epidemiological patterns exhibit notable regional differences [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, few studies have specifically addressed pulmonary infections in adult AL patients undergoing initial induction chemotherapy. This study aims to analyze the risk factors for pulmonary infection in treatment-na\u0026iuml;ve adult AL patients in the Yichang region, with the goal of informing early preventive strategies against post-chemotherapy pulmonary infections.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient population\u003c/h2\u003e \u003cp\u003eA total of 251 adult patients with newly diagnosed acute leukemia were retrospectively included in this study. These patients were hospitalized in the Department of Hematology at Yichang Central People's Hospital between September 2018 and November 2023. All participants had complete medical records and underwent the induction phase of chemotherapy at our institution. The inclusion criteria were as follows: ① age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; ② diagnosis consistent with established diagnostic criteria for acute leukemia [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; and ③ completion of induction chemotherapy at our hospital. Exclusion criteria comprised the following: ① Age under 18 years; ② Diagnosis of acute promyelocytic leukemia; ③ Pre-existing bloodstream infection, active pulmonary tuberculosis, non-infectious interstitial lung disease, pulmonary edema, pulmonary eosinophilia, or pulmonary vasculitis prior to treatment; ④ History of psychiatric or cognitive disorders; ⑤ Previous diagnosis of other malignant tumors; ⑥ Severe comorbidities involving major organs (e.g., heart, lungs, kidneys); ⑦ Coexisting hematologic diseases. Systematically collected data encompassed demographic information (age, sex), clinical features (comorbidities, length of hospital stay, acute leukemia subtype, smoking history, pre-existing comorbidities, corticosteroid use, and antibiotic exposure prior to chemotherapy), laboratory values (at diagnosis: peripheral white blood cell count, absolute neutrophil count, absolute lymphocyte count, hemoglobin, and platelet count; during chemotherapy: neutrophil-to-lymphocyte ratio, hemoglobin/leukocyte/neutrophil/platelet counts, albumin, C-reactive protein, procalcitonin, and duration of agranulocytosis [absolute neutrophil count\u0026thinsp;\u0026lt;\u0026thinsp;0.5 \u0026times; 10⁹/L]), infection details (bacterial identification), use of invasive devices (central venous catheter [CVC] or peripherally inserted central catheter [PICC]), and bronchoalveolar lavage fluid analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eThe diagnostic criteria for pulmonary infection were established based on the definitions outlined in the 10th edition of *Internal Medicine* (People's Medical Publishing House). Patients were included if they fulfilled the following criteria: ① new or worsening cough and sputum production, with or without chest pain; ② body temperature\u0026thinsp;\u0026ge;\u0026thinsp;38.0\u0026deg;C; ③ physical signs of pulmonary consolidation and/or moist rales; ④ white blood cell count\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026times;10⁹/L or \u0026lt;\u0026thinsp;4\u0026times;10⁹/L, with or without a left shift; and ⑤ chest CT findings consistent with pulmonary lesions, such as patchy, consolidative, or cavitary opacities. A clinical diagnosis of pulmonary infection\u0026mdash;encompassing infections present before chemotherapy\u0026mdash;was made if at least one of criteria (1) to (4) was met along with criterion (5), following the exclusion of other conditions such as pulmonary tuberculosis, lung malignancy, non-infectious interstitial lung disease, pulmonary edema, and atelectasis. The criteria for assessing induction remission efficacy in leukemia were defined as follows. Treatment response was classified into three categories: complete remission (CR), partial remission (PR), and non-remission (NR), with the specific criteria outlined below: Complete Remission (CR): Bone marrow blast count\u0026thinsp;\u0026lt;\u0026thinsp;5%, accompanied by the disappearance of blasts in peripheral blood and resolution of extramedullary lesions. Hematologic recovery is not a requirement for this category. Partial Remission (PR):Bone marrow blast percentage must decrease by at least 50%, resulting in a blast proportion between 5% and 25%. In addition, an absolute neutrophil count exceeding 1\u0026times;10⁹/L and a platelet count of at least 100\u0026times;10⁹/L must be achieved. Non-Remission (NR): Cases that do not satisfy the criteria for either CR or PR are classified as NR.\u003c/p\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eCategorical variables are reported as frequencies (percentages), with between-group comparisons conducted using the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test, as appropriate. Continuous data that followed a normal distribution are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared via Student\u0026rsquo;s t-test; non-normally distributed data are summarized as median (interquartile range, IQR) and analyzed using the Mann\u0026ndash;Whitney U test. To identify independent influencing factors, univariable analysis was initially conducted for all candidate variables. Variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were entered into a multivariable logistic regression model. To reduce the risk of overfitting, forward stepwise regression was employed for variable selection, using a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for both entry and removal. All statistical tests were two-sided, and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eThis study enrolled a total of 251 patients with newly diagnosed acute leukemia (AL) during their hospitalization. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, univariate analysis was performed on the adult cohort. Of the 251 patients receiving initial induction chemotherapy, pulmonary infections occurred in 200 cases, corresponding to an infection rate of 79.68% (200/251). The study population comprised 189 patients (75.3%) with acute myeloid leukemia (AML) and 62 (24.7%) with acute lymphoblastic leukemia (ALL). There were 135 males (53.8%) and 116 females (46.2%), with a median age of 55 years. A smoking history was reported in 89 patients (35.46%), among whom 80 (89.89%) developed pulmonary infection. Corticosteroid therapy was administered to 94 patients (37.45%).Of the 256 patients evaluated, 98 (39.04%) had active disease, while 153 (60.96%) had achieved remission. Univariate analysis indicated that several factors were significantly associated with pulmonary infection (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including total hospital stay, smoking history, neutrophil count before chemotherapy, prophylactic antibiotic use prior to chemotherapy, duration of agranulocytosis, nadir blood cell counts (WBC, NEC, LYM, NLR, PLT), serum albumin level, failure to achieve complete remission after chemotherapy, hypoalbuminemia, and elevated levels of C-reactive protein and procalcitonin (PCT) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Multivariable logistic regression analysis identified prolonged total hospital stay, smoking history, extended duration of agranulocytosis, and failure to achieve complete remission as independent risk factors for pulmonary infection (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, prophylactic antibiotic use before chemotherapy was found to be a protective factor (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), further corroborating the robustness of our main findings (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBacterial distribution\u003c/h2\u003e \u003cp\u003eThe primary etiological agents of pulmonary infection in patients with acute leukemia were fungi (45.98%), followed by bacteria (20.69%), mixed infections (17.24%), viruses (14.94%), and mycoplasma (1.15%). Among the bacterial isolates, Gram-positive organisms were identified in 7 cases (8.05%), comprising *Staphylococcus aureus (3, 3.45%), *Streptococcus pneumoniae (2, 2.30%), Staphylococcus epidermidis (2, 2.30%), and Staphylococcus capitis (1, 1.15%). Gram-negative bacteria accounted for 11 cases (12.64%), with the majority being Klebsiella pneumoniae (5, 5.75%), Pseudomonas aeruginosa (4, 4.60%), Escherichia coli (2, 2.30%), and Haemophilus influenzae (1, 1.15%). A total of 40 fungal pathogens were isolated, including Aspergillus (7, 8.05%), Candida albicans (1, 1.15%), Candida krusei (1, 1.15%), Candida tropicalis (1, 1.15%), *Saccharomyces cerevisiae (1, 1.15%), clinically diagnosed fungi (18, 20.69%), and suspected fungal infections (11, 12.64%). Viral pathogens were detected in 13 cases, consisting of influenza virus (7, 8.05%), SARS-CoV-2 (4, 4.60%), and Epstein-Barr virus (2, 2.30%). One case (1.15%) of Mycoplasma pneumoniae was also documented (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eunivariate of adults with newly treated acute leukemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulmonary infection cases(n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(45,64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(48,65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74(37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetestan(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlacement of picc(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116(58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroid treatment(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration of antibiotics prior to chemotherapy.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109(54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of hospitalization (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(25,35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(22,30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious use chemotherapy of blood count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.47(5.08,55.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.48(5.29,76.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(59.0,86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(50.0,88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(15.0,63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(24.0,87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39(0.39,4.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.17(1.53,6.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood count during chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38(0.21,0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10(0.60,1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(49.0,61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(46.0,61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(4.0,23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(7.0,31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02(0.01,0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25(0.06,0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte(\u0026times;10^12/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28(0.17,0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59(0.29,0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.03,0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35(0.10,0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.57(26.74,33.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.95(29.23,36.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC reactive protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.31(56.87,127.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.20(8.26,47.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePct(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28(0.12,0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12(0,0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Duration of neutropenia(days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.47(5.08,55.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(0,11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn remission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot in remission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Abbreviations: PCT: procalcitonin ;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of the risk factors for lung infection during induction chemotherapy among patients with newly diagnosed acute leukemia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of Stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.004\u0026mdash;1.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.122\u0026mdash;0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious use chemotherapy of blood count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984\u0026mdash;1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious use of Antibiotic treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.629\u0026mdash;18.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Duration of neutropenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.123\u0026mdash;1.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood count during chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026mdash;1.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.212\u0026mdash;5.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte(\u0026times;10^12/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.216\u0026mdash;1.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.523\u0026mdash;2.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026mdash;1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.986\u0026mdash;1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot in remission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.556\u0026mdash;12.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*A P-value of \u0026lt;\u0026thinsp;0.05 was considered to be statistically significant. OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDistribution of pathogens\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial Distribution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGram-negative bacteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKlebsiella pneumoniae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudomonas aeruginosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEscherichia coli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemophilus influenzae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGram-positive bacteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaphylococcus aureus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStreptococcus pneumoniae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaphylococcus epidermidis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaphylococcus capitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFungal pathogens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandida albicans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandida Cruz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandida tropical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaccharomyces cerevisiae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical diagnosis of fungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected fungal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eViral pathogens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluenza virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpstein-Barr virus (EBV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMycoplasma pneumoniae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMixed infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovel coronavirus\u0026thinsp;+\u0026thinsp;Aspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspergillus\u0026thinsp;+\u0026thinsp;human rhinovirus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovel coronavirus\u0026thinsp;+\u0026thinsp;clinical diagnosis of fungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspergillus\u0026thinsp;+\u0026thinsp;Staphylococcus epidermidis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspergillus\u0026thinsp;+\u0026thinsp;Pseudomonas aeruginosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eInfection continues to be a major contributor to morbidity and mortality in patients with acute leukemia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Induction chemotherapy often extends over a prolonged period, increasing the risk of complications such as myelosuppression. Additionally, acute leukemia directly impairs humoral and cellular immune function, weakening host defenses. The sustained administration of chemotherapeutic agents further compromises patient immunity by damaging mucosal barriers in the gastrointestinal and respiratory tracts. Among infectious complications, pulmonary infections are particularly common. A retrospective analysis revealed that pulmonary infections following induction chemotherapy for acute leukemia occur frequently, with a cumulative 28-day incidence of 21.6% in AML patients and 7.1% in those with ALL; mortality associated with these pulmonary infections reached 17% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Another retrospective study from the Japanese Leukemia Research Institute, which included 908 acute leukemia patients during induction therapy, reported bacteremia and respiratory tract infection rates of 8.3% and 10.3%, respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Existing evidence indicates considerable regional variation in infection incidence among acute leukemia patients. These geographical differences in overall infection rates are likely multifactorial, possibly influenced by individual behaviors, social environment, baseline health status, hospital resources, pathogen evolution, and population mobility [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study included 251 treatment-na\u0026iuml;ve patients with acute leukemia admitted to the Department of Hematology. Of these, 200 patients (79.68%) developed pulmonary infections, whereas 51 (20.32%) did not. Univariate analysis of patient demographic and laboratory indicators, supplemented by logistic regression, identified several independent risk factors for pulmonary infection: longer total hospital stay, smoking history, prolonged agranulocytosis duration, and failure to achieve disease remission following chemotherapy. In contrast, antibiotic administration prior to chemotherapy served as a protective factor against pulmonary infection. The hospital environment is a known reservoir for bacterial contamination. Extended hospitalization increases opportunities for pathogen colonization, transmission, and cross-infection. When pathogens invade the lower respiratory tract in acute leukemia patients, they can inflict structural and functional damage to pulmonary and tracheal tissues. Additionally, various inpatient procedures\u0026mdash;such as intubation, puncture, and surgery\u0026mdash;may heighten infection risks. In this study, the presence of a PICC line showed no statistically significant association with infection incidence, which may be closely linked to the quality of nursing care. Timely intervention and meticulous nursing have the potential to shorten hospital stays and consequently reduce infection risk. Effective disease management, appropriate preventive measures\u0026mdash;including judicious antibiotic use, maintenance of normothermia and adequate hydration, and the use of laminar airflow rooms\u0026mdash;are essential in this context. International literature indicates that early discharge is a feasible, safe, and cost-effective approach for acute leukemia patients who are in good clinical condition and free of complications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, both domestic and international studies consistently document a strong association between prolonged neutropenia and increased infection risk [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The malignant proliferation of leukemic cells in the bone marrow leads to impaired immune function. Chemotherapy may exacerbate neutropenia, thereby elevating patients\u0026rsquo; susceptibility to severe infections and increasing mortality risk. Persistent neutropenia requires vigilant monitoring, as it heightens the likelihood of bacterial, fungal, and other opportunistic infections. Owing to compromised immune defenses, such patients are at risk of rapid clinical decline and fatal infectious complications. Upon signs of infection, hospitalization and prompt empiric broad-spectrum antibiotic therapy are typically indicated, even prior to identifying the specific pathogen or infection site. In contemporary clinical practice, granulocyte colony-stimulating factor (G-CSF) is widely employed to enhance neutrophil production, thereby aiding in the prevention of febrile episodes, oral mucositis, and bacterial infections in neutropenic patients. Consistent with global findings, pre-chemotherapy antibiotic prophylaxis was identified as a significant protective factor against pulmonary infection in our cohort (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Relevant literature indicates that systemic antibacterial prophylaxis has emerged as an effective strategy for minimizing infection-related complications [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our results demonstrate that prophylactic antibiotic therapy serves as a protective factor, significantly correlated with decreased overall mortality and a lower incidence of pulmonary infection among patients with acute leukemia. Antibiotics are essential in the management of pulmonary infections, functioning by eliminating or suppressing pathogenic microorganisms. Treatment is tailored according to infection severity, pathogen profile, and patient-specific conditions, with clinicians determining the appropriate antibiotic type and dosing regimen accordingly. The selection of suitable antibiotics must account for pathogen susceptibility, antimicrobial efficacy, and individual patient factors. However, inappropriate use of antibiotics may contribute to adverse outcomes, including the emergence of drug-resistant strains [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, rational antibiotic use, guided by local antimicrobial susceptibility data and clinical presentation, is critical to mitigating antibiotic resistance and improving patient survival.\u003c/p\u003e \u003cp\u003ePatients who fail to achieve complete remission often exhibit suppressed normal hematopoiesis and are susceptible to prolonged agranulocytosis after induction chemotherapy, which contributes to increased rates of infection and mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. According to Luo et al., the pulmonary infection rate reached 81.0% among acute leukemia patients without remission [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Compared with those in complete remission, patients with persistent disease present with higher tumor burden and unimproved cellular immune function, leading to a markedly elevated risk of fatal infections. In the present study, pulmonary infections occurred in 74 of 82 patients (90.24%) with a history of smoking. A significantly higher incidence of infection was observed in smokers relative to non-smokers (P\u0026thinsp;=\u0026thinsp;0.004).Cigarette smoke consists of nicotine, nitric oxide, free radicals, acetaldehyde, and other compounds. Inhaled particles of different sizes deposit along the respiratory tract, with larger particles retained in the upper airways and finer particles penetrating to the alveolar regions. Exposure to cigarette smoke leads to morphological alterations in the bronchial mucosal epithelium, such as ciliary loss, mucus gland hypertrophy, and goblet cell hyperplasia, which can facilitate microbial proliferation and spread within the bronchi. Smoking is also known to suppress essential functions of both innate and adaptive immunity, including Toll-like receptor signaling, nuclear factor kappa B activation, CD4\u0026thinsp;+\u0026thinsp;T-cell proliferation, dendritic cell maturation, opsonization, and phagocytic activity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consequently, smoking not only modifies immune responses to infections but also disrupts intracellular and intercellular signaling pathways in epithelial and immune cells, thereby inhibiting the activation of key immune components. It is well established that smoking heightens susceptibility to infections [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, our analysis revealed that among patients with a history of smoking, fungal infections were detected in 32 cases, accounting for 74.41% (32/43) of all fungal infections in the cohort. The likelihood of fungal infection was significantly higher in smokers than in non-smokers (P\u0026thinsp;=\u0026thinsp;0.032), a result consistent with findings from international studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In cases of acute leukemia complicated by pulmonary infections, bacterial pathogens represent the most frequently identified etiological agents, with Gram-negative bacteria being predominant [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the current study, a total of 51 pathogenic isolates were identified. The pathogen distribution was as follows: bacteria were the most common, followed by viruses, fungi, and Mycoplasma. Among bacterial isolates, Gram-negative bacilli comprised the majority, representing 23.53% (12/51) of all isolates. The most frequently identified pathogen was Klebsiella pneumoniae, followed by Pseudomonas aeruginosa, Escherichia coli, and Haemophilus influenzae. Gram-positive cocci constituted 14.55% (8/55) of the isolates, including Staphylococcus aureus, Streptococcus pneumoniae, Staphylococcus epidermidis, and Staphylococcus capitis. The relatively low pathogen detection rate observed in this study may be explained by several factors: 1) suboptimal specimen quality (e.g., sputum, throat swabs) obtained from acute leukemia patients, which may have contained insufficient pathogen loads\u0026mdash;particularly if antibiotics were administered before sample collection, potentially diminishing pathogen counts; 2) delays in transporting specimens to the laboratory, which could result in pathogen degradation and false-negative outcomes; and 3) limitations associated with the detection methodologies available at our institution. Certain pathogens may be present in low abundances or require specific growth conditions not routinely used.\u003c/p\u003e \u003cp\u003eTo enhance the diagnostic yield for pulmonary infections, several strategies should be implemented. First, it is essential to raise clinical awareness among healthcare providers about the significance of microbiological diagnosis. Suspected infections should be carefully monitored, and appropriate specimens for pathogen identification must be collected promptly and repeatedly before empirical antibiotic therapy is initiated. In parallel, laboratory detection equipment and methodologies should be regularly evaluated and upgraded to improve both the detection rate and accuracy of pathogen identification. The occurrence of viral infections in patients with acute leukemia represents a serious concern that requires close attention, as these infections can precipitate rapid clinical decline and life-threatening complications. During chemotherapy, such patients are at heightened risk of viral infections due to treatment-related agranulocytosis, myelosuppression, and subsequent profound immunosuppression. Individual genetic predispositions can modulate susceptibility to particular viral pathogens. For example, specific haplotypes of the *DEFB1* gene have been linked to the prevalence of herpesviruses in serum [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. According to research by Hlaing K M et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], acute leukemia patients infected with SARS-CoV-2 are more susceptible to co-infections with other pathogens. Patients with severe or critical COVID-19 frequently receive high-dose corticosteroids and immunosuppressive therapies. Given that SARS-CoV-2 infection can directly induce lymphopenia, immune function becomes further compromised, increasing the risk of secondary infections. This interaction has prompted the emerging concept of the lung microbiome. In healthy individuals, the lungs maintain a relatively stable microbial community; however, SARS-CoV-2 infection disrupts this balance, facilitating the proliferation of other bacteria, viruses, and fungi, and leading to a dynamically altered microbiome. This shift may lead to two major clinical implications. First, the occurrence of co-infection with other pathogens complicates subsequent diagnosis and treatment. Second, interactions between SARS-CoV-2 and other pathogens can induce pulmonary immune dysregulation and worsen disease severity. Supporting evidence comes from a 2020 single-center study in Milan, Italy, which included 731 hospitalized COVID-19 patients (moderate, severe, and critical cases) and reported a secondary infection rate of 9.3% (80/731). Among these patients, bloodstream infections accounted for 7.9% (58/731) and lower respiratory tract infections for 3% (22/731). The median time to secondary infection was 12 days (IQR 8.5\u0026ndash;16.5 days), which decreased to 9 days (IQR 7\u0026ndash;17 days) in patients receiving immunomodulatory drugs. Subgroup analysis indicated that patients with baseline lymphopenia (\u0026le;\u0026thinsp;0.7\u0026times;10⁹/L) or a low baseline PaO₂/FiO₂ ratio (\u0026le;\u0026thinsp;200 mmHg) had a higher likelihood of developing secondary infections [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A multicenter study conducted in Shanghai, Wuhan (Hubei), and Tongling (Anhui) evaluated 476 COVID-19 patients by comparing clinical characteristics, laboratory results, imaging findings, and treatment regimens. Patients were categorized into four groups according to symptom severity, extent of lung involvement on chest CT, degree of hypoxemia, and need for respiratory support. The analysis revealed that disease severity was associated with an increased incidence of secondary infections [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In another investigation from Shanghai Huashan Hospital involving 38 severe or critical COVID-19 patients\u0026mdash;most of whom underwent endotracheal intubation or tracheostomy\u0026mdash;57.89% (22/38) developed secondary infections. Multivariate analysis identified invasive mechanical ventilation as a significant risk factor. Affected patients experienced considerably longer hospital stays and worse clinical outcomes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Together, these results suggest that COVID-19 patients, especially those with severe manifestations, are at elevated risk of secondary infections. Once co-infections occur, they may lead to marked clinical worsening and substantially increase mortality risk.\u003c/p\u003e \u003cp\u003eTreatment-na\u0026iuml;ve acute leukemia patients exhibit heightened susceptibility to pulmonary infections during induction chemotherapy\u0026mdash;a frequent complication closely linked to severe myelosuppression and immunosuppression. Proactive strategies to enhance immune defenses, including prophylactic antibiotic regimens, placement in laminar airflow environments, and optimized supportive care, should be implemented to reduce infection risk. Nevertheless, close monitoring remains critical, as extended antibiotic use elevates the likelihood of fungal infections, warranting serial surveillance via diagnostic assays such as G-tests and GM-tests.\u003c/p\u003e \u003cp\u003eTherefore, a personalized therapeutic strategy is essential. Meticulous monitoring of laboratory parameters before and after treatment initiation is critical to facilitate earlier and more individualized clinical interventions. In subsequent research, we intend to gather broader clinical datasets and integrate additional analytical factors\u0026mdash;such as genetic markers that may clarify the relationship between acute leukemia susceptibility and pulmonary infection risk, as well as associations between host genetics and the structure of the pulmonary microbiota. These initiatives are expected to better characterize the multifactorial determinants of pulmonary infection during induction therapy. Ultimately, risk-based patient stratification will enable the implementation of precisely tailored management approaches. Such a precision medicine framework holds considerable potential for reducing the emergence of antimicrobial resistance and meaningfully enhancing patients\u0026rsquo; quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors have not received any funding during the creation of this manuscript. The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China\u003c/p\u003e \u003cp\u003eData availability\u003c/p\u003e \u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e \u003cp\u003eEthical and consent to publish Not applicable.\u003c/p\u003e \u003cp\u003eCompeting interests The authors declare no competing interests.\u003c/p\u003e \u003cp\u003eData availability The data presented in this study are available on request from the corresponding author.\u003c/p\u003e \u003cp\u003e Declarations Ethical approval The study protocol was approved by the Institutional Review Boards (IRBs) of The First College of Clinical Medical Science of Three Gorges University (approval reference number: 2022-111-01). Since retrospective data were used, the IRB of The First College of Clinical Medical Science of Three Gorges University waived the requirement for informed consent. This research complied with the ethical principles for medical research declared by the World\u003c/p\u003e \u003cp\u003eMedical Association, and the study was conducted in compliance with the Helsinki Declaration of 1964 and all of its amendments. All patient data was kept confidential and secured. The collected patient informa tion was used only for research purposes. Consent for publication Not applicable. Competing interests The authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLi-jing Wang and Hui-li Cai designed the study. Li-jing Wang analyzed the data and generated the tables and figures. Qi Gao, Bo Pang, Tao Wu, Jun Xiang，Liming Xiang ，Kuanxin Zhang collected and recorded the data. Li-jing Wang wrote the study. Hui-li Cai: Conceptualization, project administration, Writing\u0026ndash; review \u0026amp; editing. Confficts of interest. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLadikou EE, Ashworth I, Seviar D et al (2022) Acute leukaemia: no reason to panic. Clin Med (Lond) 22(3):221\u0026ndash;224\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong X, Ma H (2022) Targeting CD38 for acute leukemia. Front Oncol 12:1007783\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulanovich V, Kontoyiannis DP (2018) Acute myeloid leukemia and the infectious diseases consultant. Leuk Lymphoma 59(6):1284\u0026ndash;1291\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLogan C, Koura D, Taplitz R (2020) Updates in infection risk and management in acute leukemia. 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Curr Oncol Rep 24(6):671\u0026ndash;686\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTjonnfjord SKV, Ali MM, Tjonnfjord GE (2023) Chronic neutropenia in adults. Tidsskr Nor Laegeforen. ;143(9)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadhakrishnan V, Lagudu PBB, Gangopadhyay D et al (2022) Neutropenic versus regular diet for acute leukaemia induction chemotherapy: randomised controlled trial. BMJ Support Palliat Care 12(4):421\u0026ndash;430\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDufrayer MC, Monteiro YMC, Carlesse FADM et al (2023) Antibiotic prophylaxis in acute childhood leukemia: What is known so far? Hematol Transfus Cell Ther 45(4):473\u0026ndash;482\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan K, He S, Kuang X et al (2022) [Analysis of characteristics, influencing factors and risk prediction model of pulmonary infection after chemotherapy in patients with acute leukemia]. Zhonghua Yi Yuan Gan Ran Xue Za Zhi 32(8):1144\u0026ndash;1148\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Lin Q, Liu L et al (2020) Risk Factors and Outcomes of Antibiotic-resistant Pseudomonas aeruginosa Bloodstream Infection in Adult Patients With Acute Leukemia. Clin Infect Dis 71(Suppl 4):S386\u0026ndash;S393\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryant AL, Deal AM, Walton A et al (2015) Use of ED and hospital services for patients with acute leukemia after induction therapy: one year follow-up. Leuk Res 39(4):406\u0026ndash;410\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Y, Liu T, Xie S et al (2015) [Clinical features and risk factors for infections in adult acute leukemia after chemotherapy]. Zhonghua Xue Ye Xue Za Zhi 36(12):1020\u0026ndash;1024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmirall J, Blanquer J, Bello S (2014) Community-acquired pneumonia among smokers. Arch Bronconeumol 50(6):250\u0026ndash;254\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhandelwal A, Chaudhary SC, Verma AK et al (2023) Nontubercular bacterial and fungal infections in patients of chronic obstructive pulmonary disease. Ann Afr Med 22(1):77\u0026ndash;81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStemler J, Mellinghoff SC, Khodamoradi Y et al (2023) Primary prophylaxis of invasive fungal diseases in patients with haematological malignancies: 2022 update of the recommendations of the Infectious Diseases Working Party (AGIHO) of the German Society for Haematology and Medical Oncology (DGHO). J Antimicrob Chemother 78(8):1813\u0026ndash;1826\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou XL, Ma D (2024) [Analysis of risk factors for pulmonary infection after chemotherapy in patients with acute leukemia and establishment of a risk nomogram model]. Shi Yong Yu Fang Yi Xue 31(1):39\u0026ndash;43\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmadzadeh A, Varnasseri M, Jalili MH et al (2013) Infection Pattern of Neutropenic Patients in Post-chemotherapy Phase of Acute Leukemia Treatment. Hematol Rep 5(4):e15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoschovi M, Adamaki M, Vlahopoulos SA (2016) Progress in Treatment of Viral Infections in Children with Acute Lymphoblastic Leukemia. Oncol Rev 10(1):300\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHlaing KM, Monday LM, Nucci M et al (2023) Invasive Fungal Infections Associated with COVID-19. J Fungi (Basel). ;9(6)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRipa M, Galli L, Poli A et al (2021) Secondary infections in patients hospitalized with COVID-19: incidence and predictive factors. Clin Microbiol Infect 27(3):451\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng Y, Ling Y, Bai T et al (2020) COVID-19 with Different Severities: A Multicenter Study of Clinical Features. Am J Respir Crit Care Med 201(11):1380\u0026ndash;1388\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Zhang Y, Wu J et al (2020) Risks and features of secondary infections in severe and critical ill COVID-19 patients. Emerg Microbes Infect 9(1):1958\u0026ndash;1964\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute leukemia, pulmonary infection, risk factors, smoking history, novel coronavirus infection","lastPublishedDoi":"10.21203/rs.3.rs-8621539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8621539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTis study investigated the factors influencing the development of pulmonary infections during initial induction chemotherapy in patients with acute leukemia in Yichang area, which has certain guiding significance for the early prevention of pulmonary infection after chemotherapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed the clinical data and laboratory findings of patients newly diagnosed with acute leukemia, admitted to the Department of Hematology at Yichang Central People's Hospital between September 2018 and November 2023. Risk factors for pulmonary infection and the distribution of pulmonary pathogens were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 251 patients with acute leukemia were enrolled in the study, comprising 62 cases of acute lymphoblastic leukemia and 189 cases of acute myeloid leukemia. Pulmonary infections developed in 200 patients, corresponding to an infection rate of 79.68%. Multivariate analysis revealed that prolonged hospitalization, smoking history, extended duration of agranulocytosis, and failure to achieve complete remission following chemotherapy were independent risk factors for pulmonary infection (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, prophylactic antibiotic use prior to chemotherapy was identified as a protective factor. Among the 200 patients with pulmonary infections, 51 pathogenic isolates were identified. The distribution of pathogens was as follows: bacteria (39.22%), viruses (34.55%), fungi (20.0%), and Mycoplasma (1.82%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn patients receiving initial chemotherapy for acute leukemia, prolonged hospitalization, extended duration of agranulocytosis, failure to achieve complete remission, and a history of smoking were associated with an elevated risk of pulmonary infection. Gram-negative bacteria represented the predominant pathogens. Early identification, timely intervention, and appropriate selection of antimicrobial agents are critical to reducing mortality in this population.\u003c/p\u003e","manuscriptTitle":"Analysis of risk factors for pulmonary infection in adults with newly treated acute leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 10:11:28","doi":"10.21203/rs.3.rs-8621539/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6ecca00-e680-46c4-a332-874bff137b13","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T06:09:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 10:11:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8621539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8621539","identity":"rs-8621539","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}